Two McGill researchers argue that pacing frontier AI only works if the extra time is spent on anticipatory thinking. We check their case against the summer’s agent breaches involving OpenAI, Anthropic, Meta and the UK AI Security Institute, and show how teams can turn possible failures into tests.
A practical reference for the four families of AI agent evaluation metrics: accuracy, cost, safety and reliability. Includes computable definitions, starter thresholds, evaluation-set design, LLM-as-judge validation, and how to wire the numbers into a release gate.
A practical playbook for testing AI agents before launch: build a real evaluation set, red-team prompt injection and tool abuse, prove reliability under load, and gate every release on objective checks.
Why AI Agents Give Inconsistent Results is explained by the complete agent system, including instructions, retrieved information, memory, tools, permissions, orchestration, and runtime conditions.
A practical explanation of why claims about AI worldview convergence weaken when high-dimensional embeddings, cosine similarity, hubness, and weak baselines skew similarity scores.
